Method and apparatus for measuring product shipment process capability

ABSTRACT

A system and method for automatically assessing the process quality capability of a shipping process is disclosed. The system monitors data on a database that is being constantly updated with information about shipment requests and shipment dates for various products. From that data, a statistical calculation is performed and the results are indicated on an Internet or intranet electronic page. The statistical calculation is designed to indicate the capabilities of the current process to deliver products to customers on time.

BACKGROUND OF THE INVENTION

The present invention relates generally to the use of statistics tomeasure the performance and performance capabilities of a process, andmore particularly, to automated electronic reporting of the real-timeresults of statistical calculations intended to indicate futureperformance.

There have been many improvements toward attaining quality goals of anybusiness, and a large number of those relate to manufacturing facilitiesand methods. For example, the quality of automobiles has steadilyincreased over the years in part due to improved methods for increasingthe quality of the manufacturing process.

A focus on shipment timeliness quality can also improve the delivery ofproducts or services to the customer. Customer satisfaction can hinge onbasic deliverables such as on-time installation, or quick phone responsewhen called for technical assistance. This is also true for the verysimplest of business tasks like delivering the product or service whenthe customer was told to expect it.

In manufacturing, for example, process performance has traditionallybeen measured by sampling the output to ensure compliance within thespecification limits. A sample of the output is taken and analyzed atthe source to determine how the process is working and the adjustmentsthat should be made if deemed necessary. This method has a number ofdrawbacks, not the least of which is timeliness of the information.

Process capability is provided by some well-known measurements in theart of statistics. Some generally known and accepted measurements forprocess capability Include: Z long-term, Z short-term, Z bench, C_(PK),C_(P), sigma value, and defects per million opportunities. Each of thesevalues provides a measure of the capability of a process. That is, theability of the process to produce quality output over time. Givenup-to-date information, a manager or process-owner can make adjustmentsor further investigate ways to improve the process thereby improving thequality of the output.

It would therefore be desirable to have an electronic system able toprovide information indicative of process capability on a real-time,on-demand basis.

SUMMARY OF THE INVENTION

The invention is directed to providing real-time, information aboutprocess capability, or in a time that for all practical purposes isreal-time to the user. The information is to be available continuouslyand automatically updated at a frequency to maintain the real-timeeffectiveness of the information. The preferred embodiment provides theinformation electronically upon request to a terminal or computer thatis in communication with the relevant network.

In accordance with one aspect of the invention, a method for measuringproduct shipment process capability is disclosed. The method uses adatabase that is maintained with current data and contains fieldsindicating an order number, a maximum ship date, a customer requesteddate, and a product category for each order. The maximum ship date isthe date when the last piece of an order was shipped, and the customerrequested date is the date when the customer requested the product to beshipped. The method requires fetching order information for all ordersthat have a valid maximum ship date, subtracting the customer requesteddate from the maximum ship date thereby producing a difference value,adding a predetermined number of days to the difference value to providea shipment quality metric for each order, and using the shipment qualitymetric in a statistical calculation to indicate process quality.

In accordance with another aspect of the invention, a computer-readablemedium is disclosed. The computer readable medium contains one or morecomputer programs that, when executed by one or more computers, causesthe one or more computers to follow a number of instructions from thecomputer program. The one or more programs first instruct the one ormore computers to query a database that contains information detailingorders, a requested delivery date, a maximum ship date, and a productcategory for a plurality of products. Any orders with an invalid entryfor the maximum ship date or absent an entry for the maximum ship dateare ignored. The one or more computers are then instructed to subtractthe requested delivery date from the maximum ship date and add anadjustment value to obtain a shipment quality metric. The query,subtraction, and addition acts are repeated for a plurality of shippedproducts until all are processed. At this point the one or morecomputers process the shipment quality metrics to determine overallshipment quality.

In accordance with yet another aspect of the invention, a computer datasignal is disclosed. The signal represents a sequence of instructionsthat, when executed by one of more processors, causes the one or moreprocessors to accomplish a number of tasks. The tasks involve a databasethat is maintained with data indicating order numbers, promise dates,request dates, maximum ship dates, and a product category for a numberof products. The instructions then provide for obtaining the data fromeach order that has a valid maximum ship date. That data is used tocreate an upper specification limit by adding a predetermined number ofdays just prior to a customer's requested delivery date, and to create alower specification limit by adding a predetermined number of days aftera customer's requested delivery date. The data signal then causes theone or more processors to compute and display a statistical valueproviding an indication of process capability that is then relayed inthe data signal to the user.

Other features, objects, and advantages of the present invention will bemade apparent by the following detailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate one mode presently contemplated for carrying outthe invention.

FIG. 1 is a high-level overview block diagram representing an embodimentof the invention.

FIG. 2 is a flow chart representation of a process to display qualityindicators in accordance with one aspect of the invention.

FIG. 3 is a flow chart representation of the availability reportingprocess in accordance with another aspect of the invention.

FIG. 4 is a flow chart representation of the work in progress tableupdate process in accordance with another aspect of the invention.

FIG. 5 is a flow chart representation of the shipment and promise alertsetting and display process in accordance with another aspect of theinvention.

FIG. 6 is a flow chart representation of the process to display ordersand revenue in accordance with another aspect of the invention.

FIG. 7 is a representation of the Z-value graphic indicator inaccordance with another aspect of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In a preferred embodiment of the present invention, a program isdesigned to augment a commercially available database program.Specifically, it is designed as a separate program that works with datain the database. The program provides real-time, or near real-timereporting capabilities that are not available to users of thecommercially available database. Other embodiments of the invention mayuse different hardware and/or software manifestations to embody theinvention in accordance with their particular design.

Referring to FIG. 1, an overview diagram of a reporting system is shownwhich includes a plurality of user stations 10 such as User A, User Bthrough User Z. These user stations 10 represent any number of users ona network at any time. The user stations 10 are connected to an Intranetserver 12. This can be through direct communication, a local areanetwork, or from another network like an intranet or the Internet. Itmay also include user stations 10 connected via a variety of othernetworking connections, including wireless connections. The Internetserver 12 is in communication with database 14, which may be on anothercomputer network. The Intranet server 12 is also communicativelyconnected to a mainframe/processing section 16. The Mainframe/Processingsection 16 processes data from database 14 based on user requests andcompletes various reports and provides the reports to the user stations10.

Database 14 contains data on orders, availability (when products will beavailable for shipping), requested shipping dates, actual shippingdates, promised shipping dates, revenue per order, and various otherproduct and sales information. All of the information is constantlyupdated at the user stations 10, and is communicated through theIntranet server 12. Some of the users may update the information andsome may merely be accessing the database for information. The boxes20-28 in FIG. 1 show the input of this information into the database 14,indicated by data input 18. The data includes new orders 20, updateorder status 22, current inventory 24, product status 26, and shipmentdata 28.

The database 14 has the ability to reserve areas for computationalresults known as temporary tables 30. The tables contain the latesttotals programmed to be stored therein that can be readily accessed bythe users at user stations 10. It is temporary in that the temporarytable processing system 32 repeatedly updates the temporary tables 30.When the data in the database 14 changes, the inputs to the temporarytable processing system 32 are modified to update the information in thetemporary tables 30 with new totals. In the preferred embodiment, thetemporary table processing system 32 updates the data in the temporarytables 30 every 60 minutes, but the time interval can be set by theprogrammer. Depending on needs, the update is performed based on suchfactors as product turn over, product manufacture time, number of orderstaken per interval, and so on. The update should be performed oftenenough so that users acquire real-time data based on these factors.

The preferred embodiment shown in FIG. 1 contemplates a manufacturingfacility that has a number of products in various stages of completion,or a re-manufacturing facility with the same traits. Such a facility mayhave a system as represented by FIG. 1 merely to report the status tointernal users. Additionally, an organization with these capabilitiesmay want to make the information available to field sales personal, oreven directly to potential customers.

FIG. 2 is a representation of a preferred embodiment for the statisticalprediction of future performance process. In general, a process can bemeasured for performance if certain specification limits are set tomeasure it. To do so, the system must determine how many times theprocess produces a defect, or an output, that does not conform to thespecifications that were set, as compared to how many times it producesa non-defect, or a successful output, within a given number ofopportunities. In this manner, it is possible statistically to predictthe output of a process by sampling performance of the process andanalyzing that data. The process can then be assessed and adjusted toimprove future performance as against the specification limits.

Still referring to FIG. 2, the flowchart provides a description of apreferred process for creating the statistical calculation to indicatequality. FIG. 2 is divided into two separate parts. Part A describesupdating the temporary tables 30 in the database 14, and Part Bdescribes the process flow of the statistical calculation.

Now referring to FIG. 2, Part A, in more detail, upon initialization ofthe updating process 34, data from the database 36 is obtained by afetch order for information 38. This is sometimes referred to asquerying the database. In this embodiment, the data obtained is only forthe orders that have occurred within the past year for statisticalpurposes. However, any time period can be chosen based on design choice.In the next step orders that have not been shipped are removed fromconsideration 40 because this portion of the system is only concernedwith shipped orders. Next, the initial calculation 42 is performed whichrequires fetching the maximum ship date from that order at 44 andfetching the customer's requested date for delivery 46. In thisembodiment, each order may have more than one product, and the maximumship date referred to in 44 is the date that the last product on theorder was actually shipped. The customer requested date for delivery 46is subtracted 48 from the maximum ship date 44. At this point, theprocess adds 52 the time needed for shipping the product to the customer50 to the difference between the maximum ship date and the customer'srequested date for delivery providing the result to update the table 54.Updating the information in the table or providing a new entry thenmodifies the table. The process then checks whether the last order wasprocessed 56, and if not 56 a, the process returns to the initialcalculation step 42 where the previously-described process is repeatedon the next order entry. If the order is the last 56 b, then the processcontinues to the statistical calculation section of part B. It isimportant to note that the invention anticipates the use of any numberof statistical calculations to predict the capability of a process, bothknown and as yet unknown. The preferred embodiment uses a value known asthe “Z” score to provide information about process capability.

To calculate the mean 58, the data is added together and divided by thenumber of entries. In statistical equations the mean is customarilyrepresented by the Greek letter mu (μ). The next act in the process isto calculate the variance 60. As is well known in the art, the varianceis calculated by subtracting the mean from each entry in the table todetermine a deviation for each entry, squaring each deviation, summingthe squared deviations, and dividing that sum by the number of entries.The Greek letter sigma σ represents variance when shown with an exponentof 2 (i.e., σ²). The next act is to calculate the standard deviation 62.This calculation is also well known in the art, and it is equal to thesquare root of the variance. The standard deviation is traditionallyrepresented by the Greek letter sigma (σ) with an exponent of 1. Thenext step is to determine the values for Z long-term and Z short-term64. In general, Z-scores are well known in the art of statistics. Zlong-term (Z_(LT)) is calculated from the standard deviation and theaverage output of the current process. Used with continuous data, Z_(LT)represents the overall process capability and can be used to determinethe probability of out-of-specification parts within the currentprocess. Usually process capability is measured in defective parts permillion opportunities, or DPMO.

In the preferred embodiment, the Z score is determined by first settingan upper specification limit (USL) and a lower specification limit(LSL), also referred to as tolerance limits. These limits are designatedeither arbitrarily, or as a result of researching customer needs, todetermine the goals or tolerance of the process. As a measure ofquality, any data point that falls outside of the USL and LSL is thenconsidered a defect. The Z long-term (Z_(LT)) value is then calculatedby use of the formula:

${Z_{LT} = {\min\left\lbrack {\frac{{USL} - \mu}{\sigma},\frac{\mu - {LSL}}{\sigma}} \right\rbrack}},$where USL is the preset upper specification limit, LSL is the presetlower specification limit, μ is the mean, and σ is the standarddeviation. In the preferred embodiment, the minimum result of the twoexpressions is taken as the measure of performance for the process. Theuser may then interpret the result. Interpretation may include applyingthe result to a normal or other distribution to determine the percentageof defects produced with a given number of opportunities. It iscontemplated and believed within the scope of the present invention thatinterpretation of the results to determine DPMO could be easilyautomated as well. Z values can also be calculated in other ways; forexample, both expressions can be used in some calculations instead ofusing the minimum of the two.

Continuing with the preferred embodiment, the Z_(LT) value is then usedto determine the Z short term (ZST) value by using the formula:Z_(ST)=Z_(LT)+1.5. This is an estimation of performance based upon theidea that the performance of a process will deteriorate over time. Thusthe short-term performance represented by the Z score should be betterthan the long-term performance, and adding 1.5 to the long-term Z scoreestimates the short-term Z score. The Z scores are then moved to theupdate table 66 where they can be called for display. In the preferredembodiment, Z short term (Z_(ST)) is the standard scale for reportingperformance quality based on a target goal of 6 Sigma (If Z_(ST)=6 thenDPMO=3.4). At this point the process ends 68.

Accordingly, the present invention includes a method for measuringproduct shipment process capability that includes maintaining a databasehaving therein data fields indicative of at least an order, a maximumship date, a customer requested data, and a product category for eachorder, and then fetching order information for all orders that have avalid maximum ship date. The method includes subtracting the customerrequested date from the maximum ship date and producing a differencevalue therefrom. Next, a predetermined number of days is added to thedifference value to provide a shipment quality metric for each order.The method next includes using the shipment quality metric in astatistical calculation to indicate process quality.

In a preferred embodiment, the order information fetched from thedatabase is only for those orders that were placed within a given timeperiod. The statistical calculation can include determining a value foran upper specification limit and a lower specification limit, and thendisplaying the percentage of times the shipment quality metric wasgreater than the upper specification limit and displaying the percentageof time the shipment quality metric was less than the lowerspecification limit. The invention can include setting a value for atleast one specification limit and computing and displaying a statisticalscore based upon the specification limit and the shipment qualitymetrics where the statistical score is a measure of process capability.The process includes periodically repeating the fetching, subtracting,adding shipping days, and determining a statistical calculation atregular time intervals. The statistical calculation is preferablycalculated and displayed for each product category. Further, the Zvalues can be displayed on a scale representing a range of Z values withan overlapping needle to indicate current performance as will bediscussed later in reference to FIG. 7.

The invention also includes a computer program implementing theaforementioned method. The computer program is stored on a computerreadable medium and causes one or more computers to query a databasethat contains the information detailing orders, a requested deliverydate, a maximum ship date, and a product category for a number ofproducts. In processing the data, the computers are caused to ignoreorders with no maximum ship date, and subtract the requested deliverydate from the maximum ship date and add an adjustment value to obtain ashipment quality metric. The query, subtraction, and addition acts arerepeated for the number of shipped products, and the computer programhas instructions to process the shipment quality metrics to determineoverall shipment quality.

Preferably, the shipment quality metrics are processed to provide astatistical measure of process capability and are regularly reprocessedby repeating the aforementioned acts at regular time intervals. Theregular time intervals are dependent upon the system being evaluated andare preferably set such that the user of the system perceives that theshipment quality metrics are updated in real-time.

Preferably, the set of instructions in the computer program that processthe shipment quality metrics includes instructions to determine a meanand a standard deviation of the shipment quality metrics, and todesignate an upper and lower specification limits, and then to determinea Z long-term value by subtracting the mean from the upper specificationlimit and dividing the result by the standard deviation. The Z long-termvalue is then displayed. Further instructions can includes an estimatedvalue for Z short-term by adding a constant to the Z long-term value.

Another embodiment of the invention includes a computer data signalrepresenting a sequence of instructions that, when executed by aprocessor, cause the processor to maintain a database of data havingtherein an order number, a promise date, a request date, a maximum shipdate, and a product category for each product. The instructions in thecomputer data signal cause the processor to obtain the data from eachorder that has a valid maximum ship date and create an upperspecification limit by adding a predetermined number of days just priorto a customer's requested delivery date and to create a lowerspecification limit by adding a predetermined number of days after acustomer's requested delivery date. The processor is then caused tocompute and display a statistical value providing an indication ofprocess capability. These instructions are periodically repeated eitherat regular time intervals, in real-time, or on a on-demand basis thatcan include recalculating the statistical value each time a userrequests the information. In order to calculate the statistical value,the computer data signal preferably has instructions to determine a meanvalue and a standard deviation and to subtract the mean value from theupper specification limit and divide a result by the standard deviationto create a first Z-value. The lower specification limit is subtractedfrom the mean value and the result is divided by the standard deviationto create a second Z-value. The minimum of the two values is then chosenas the statistical value.

The computer data signal can also have instructions to project what thenumber of defects would be, given one million opportunities. The defectsper million (DPM) is based upon a normal distribution. Applying theZ_(LT) or Z_(ST) to a normal distribution table provides the predictedDPM. While the current preferred embodiment does not describe a methodto do this electronically, it can easily be done by the use of a simplelook-up table, well known in the art of computer programming. The Z_(LT)or Z_(ST) value is found on the table and the associated DPM value isobtained. The DPM value is then displayed as the number of defects perone million opportunities. The instructions in the computer data signalcan also cause a processor to decide which of the first and secondZ-values are a minimum value and then to display the minimum valueidentified as a Z long-term value. A constant can then be added to the Zlong-term value to determine and display a Z short-term value.

FIG. 3 is a flow diagram of the process of a preferred embodiment fordisplaying inventory availability. The process begins at the start step70 and the first step is to fetch all saleable items in the inventory 72from the database. The program then must determine whether each recordcontains a date indicative of whether the product is available to ship,referred to as an available to promise (ATP) date 74. If a particularrecord does not have an ATP date 74 a, the record is ignored 76. If itdoes have an ATP date 74 b, then the process next counts the number ofdays between a current date (i.e., typically today) and the ATP date at78. The resulting number is then provided to the next part of theprocess to determine which of the messages is appropriate to display forthis record. If the number of days between the current date and the ATPdate is greater than 500 days 80, then the message “Call forAvailability” is displayed 82. If the number of days between the currentdate and the ATP date is greater than 2 days and less than or equal to500 days 84, then the message “Shipment within number where number isthe number of days between the current date today and the ATP date days”86 is displayed. On the other hand, if the number of days between thecurrent date today and the ATP date is less than or equal to two 88,then the message “Immediate Shipment” is displayed 90. Once theappropriate message is displayed, the process ends for that entry at 92.

FIG. 4 is a flow diagram representing the work in progress (WIP) tableupdate process for one preferred embodiment. The process updates the WIPtable that is in the temporary tables of the database to ensure the datadisplayed is the most current available. The process begins afterinitialization 93 by fetching booked orders that have been promised ashipping date 94. The system then determines whether the record alreadyexists in the current WIP table 96. If the record already exists 96 a,the order number, order promise date, request date, and the productcategory are fetched 98, and they are used to update the current entryin the WIP table 100. This portion of the process is then complete at101. If the record does not exist 96 b, the order number, order promisedate, request date, and the product category are fetched 102, and areused to create a new entry in the WIP table 104. This portion of theprocess is then complete at 101. The entire process repeats until allthe entries for the WIP table have been either updated or created.

FIG. 5 is a flow chart representing the alert setting and displayprocess. The system provides for both proactive and reactive alerts,allowing use of the information to repair problems in the shippingprocess. The word “proactive” is meant to show that an alert may be setwhile there is still time to rectify a possible problem in the process,in this case, shipping. The proactive alert allows the process owners tomake adjustments or take special action in order to avoid a lateshipment. The reactive alert may not provide the same information, butthe information is still useful to ensure that recognized problems donot occur.

Continuing with FIG. 5, the alert setting and display process beginsafter an initialization 105, by fetching the WIP table 106 and thenprocessing with each order in the WIP table. The program fetches therespective product category 108 and then the maximum promise date 110.The maximum promise date is the latest date promised to the customer forshipment. The request date is then fetched 112, and then the processdetermines whether the promise date is after the request date 114. Ifthe promise date is later than the request date 114 a, then a “PromiseAlert” is set and displayed for that order 116, and the process moves tothe order already shipped question 118. If the maximum promise date isprior to the request date 114 b, then the process skips the promisealert step 116 and moves directly to the order already shipped 118determination. If the order has already been shipped 118 a, the processmoves directly to check to see if all the orders have been processed at124. If not 118 b, the process then determines whether the request dateis within a preset number of days from a current data 120. In thisembodiment, the preset number of days is two. If the request date is notwithin two days of the current date 120 a then the process moves tocheck if another order must be processed at 124. If the request date iswithin two days of the current date 120 b, then the “Ship Alert” is setand displayed 122, and the process moves to check if all the orders havebeen processed 124. The process then repeats for all orders 124, 124 a.Once all of the orders have been processed 124 b, the alerts areaccumulated and displayed for the individual orders sorted by productcategory and type of alert 126. The alerts provide opportunities formanagement to fix problems before they happen.

FIG. 6 is a representation of the process used to display the orders andrevenue for a past period of time. The process is initiatedautomatically when the information is requested, or on a regularpre-programmed basis 127. In a preferred embodiment, all of the orderinformation, including revenue for each order, is retrieved from thedatabase 128. The program then captures all the orders 130 in a previousperiod of time, in this example, a last year. The Orders table is thenupdated 132 with the data from the capture 130. At this point the ordersin the table are sorted by month and category 134. The last stepprovides that the orders, order totals, revenue and revenue totals aredisplayed in tabular format 136 for viewing in an easily understandableway. After all the orders are displayed the process is completed at 138.

FIG. 7 is a representation of a Z-value graphic indicator 140 inaccordance with another aspect of the invention. The ranges of values onthe scale 142 change automatically to best reflect the value to beindicated, and a needle 144 indicates the value of the currentcalculation.

Each of the processes just described can be used alone, or inconjunction with each other in various combinations. In one preferredembodiment, the reports are displayed on a number of web pages within anintranet system. It is automatically updated and the information isavailable 24 hours a day on an as-needed, on-demand basis.

The present invention has been described in terms of the preferredembodiment. While the preferred embodiment uses computers that arecommunicating through some form of a network, it is understood thatother embodiments of the invention may involve the use of differenttechnologies. It is recognized that equivalents, alternatives andmodifications that are different from the preferred embodiment exist,and they are within the scope of the appending claims.

1. A method for measuring product shipment process capability,comprising: maintaining a database that contains fields indicating atleast an order, a max ship date, a customer requested date, and aproduct category for each order; fetching order information for allorders that have a valid max ship date; subtracting the customerrequested date from the max ship date producing a difference value;adding a predetermined number of days to the difference value providinga shipment quality metric for each order; and determining a statisticalcalculation to indicate process quality using the shipment qualitymetric wherein the steps of maintaining, fetching, subtracting, addingand determining are performed by a computer.
 2. The method of claim 1wherein the order information fetched from the database is only forthose orders that were placed within a given time period.
 3. The methodof claim 1 further comprising: determining a value for an upperspecification limit and a lower specification limit; displaying apercentage of times the shipment quality metric was greater than theupper specification limit; and displaying a percentage of times theshipment quality metric was less than the lower specification limit. 4.The method of claim 1 further comprising: setting a value for at leastone specification limit; and computing and displaying a statisticalscore based upon the specification limit and the shipment qualitymetrics, wherein said statistical score is a measure of processcapability.
 5. The method of claim 4 wherein the statistical score iscalculated by using a formula given by:$Z_{LT} = {{\min\left\lbrack {\frac{{USL} - \mu}{\sigma},\frac{\mu - {LSL}}{\sigma}} \right\rbrack}.}$6. The method of claim 5 wherein the method further comprisesdetermining Z short-term by use of the formula Z_(ST)=Z_(LT)+1.5.
 7. Themethod of claim 6 wherein the method further comprises graphicallydisplaying the Z_(ST) value by displaying a range of values with anoverlapping needle to indicate current performance.
 8. The method ofclaim 5 wherein the method further comprises displaying said Z_(LT)value by displaying a scale representing a range of values for Z_(LT)with an overlapping needle to indicate current performance.
 9. Themethod of claim 1 wherein the steps following maintaining the databaseare repeated at regular time intervals.
 10. The method of claim 1wherein the statistical calculation is calculated and displayed for eachproduct category.
 11. A non-transitory computer-readable medium havingstored thereon one or more computer programs having a set ofinstructions that, when executed by one or more computers, causes theone or more computers to: query a database that contains informationdetailing orders, a requested delivery date, a max ship date, and aproduct category for a plurality of products; ignore orders with no maxship date; subtract the requested delivery date from the max ship dateand add an adjustment value to obtain a shipment quality metric; repeatthe query, subtraction, addition acts for a plurality of shippedproducts; and process the shipment quality metrics to determine overallshipment quality.
 12. The computer-readable medium of claim 11 whereinthe shipment quality metrics are processed to provide a statisticalmeasure of process capability.
 13. The computer-readable medium of claim11 wherein the shipment quality metrics are regularly re-processed byrepeating the acts of claim 11 at regular time intervals.
 14. Thecomputer-readable medium as in claim 13 wherein the regular timeinterval is substantially real-time as perceived by a user.
 15. Thecomputer-readable medium of claim 11 wherein processing the shipmentquality metrics is accomplished by a set of instructions that, whenexecuted by one or more computers, causes the one or more computers tofurther: determine a mean of the shipment quality metrics; determine astandard deviation of the shipment quality metrics; designate an upperspecification limit (USL) and a lower specification limit (LSL) for theshipment quality metrics; determine a Z long-term value by subtractingthe mean from the upper specification limit and dividing the result bythe standard deviation; and display the value of Z long-term.
 16. Thecomputer-readable medium of claim 15 having further instructions todetermine an estimated value for Z Short Term by adding a constant tothe Z long-term value.
 17. A non-transitory computer readable mediumincluding a sequence of instructions that, when executed by one of moreprocessors, cause the one or more processors to: maintain a database ofdata indicating an order number, a promise date, a request date, a maxship date, and a product category for each product; obtain the data fromeach order that has a valid max ship date; create an upper specificationlimit by adding a predetermined number of days just prior to acustomer's requested delivery date; create a lower specification limitby adding a predetermined number of days after a customer's requesteddelivery date; and compute and display a statistical value providing anindication of process capability.
 18. The non-transitory computerreadable medium of claim 17 wherein the computer data signal containsfurther instructions to repeat the instructions of claim 17 at regulartime intervals.
 19. The non-transitory computer readable medium of claim17 wherein the information is updated and the statistical value isrecalculated every time a user requests the information.
 20. Thenon-transitory computer readable medium of claim 17 having instructionsto: determine a mean value and a standard deviation; subtract the meanvalue from the upper specification limit and divide a result by thestandard deviation to create a first Z-value; subtract the lowerspecification limit from the mean value and divide a result by thestandard deviation to create a second Z-value; and choose a value thatis a minimum of the first and second Z-values.
 21. The non-transitorycomputer readable medium of claim 20 wherein the instructions cause theone or more processors to further: decide which of the first and secondZ-values are a minimum value; and display the minimum value first andsecond Z-values identified as Z long-term.
 22. The non-transitorycomputer readable medium of claim 21 wherein the instructions cause theone or more processors to further: add 1.5 to the minimum value anddisplay it as Z short-term.
 23. The non-transitory computer readablemedium of claim 17 wherein the statistical value calculated anddisplayed is a projected defect in parts per million.
 24. Thenon-transitory computer readable medium of claim 17 wherein thestatistical value calculated and displayed is a Z long-term value. 25.The non-transitory computer readable medium of claim 17 wherein thestatistical value calculated and displayed is a Z short-term value. 26.The non-transitory computer readable medium of claim 17 havinginstructions to: determine a number of times that an actual shipmentdate was between the upper specification limit and the lowerspecification limit given a number of opportunities; project a number oftimes that a shipment date would not be between the upper specificationlimit and the lower specification limit given one million opportunities;and display the projected number as defects per one millionopportunities.
 27. A non-transitory computer readable storage mediumhaving a computer program stored thereon which, when executed by aprocessor, causes the processor to: acquire a requested delivery dateand a shipped date of a number of customer orders from a database;calculate a shipment metric mean value and standard deviation from thedates; establish an upper specification limit and a lower specificationlimit; calculate a first Z value by subtracting the mean value from theupper specification limit and dividing by the standard deviation;calculate a second Z value by subtracting the lower specification limitfrom the mean value and dividing by the standard deviation; anddetermine a long term process capability value by selecting a minimum ofthe first Z value and the second Z value.